Why This Matters

If you're building or buying an enterprise knowledge system, the RAG architecture matters more than the LLM choice.

• Traditional RAG is fundamentally flawed:
• • One-shot retrieval with no verification
  • No self-correction when wrong documents are retrieved
  • Cannot handle multi-hop reasoning ("find contracts that depend on budgets approved by X")
  • Hallucination risk ~10-15% even with retrieval
    
• Agentic RAG solves these problems:
• • Multi-agent orchestration with specialized roles
  • CRAG (Corrective RAG) self-correction loops
  • GraphRAG for relationship-aware retrieval
  • Verified accuracy with paragraph-level citations
• Bottom line: If you're in a regulated industry or accuracy is mission-critical, traditional RAG is not sufficient. You need agents.

How Traditional RAG Works

The Standard RAG Pipeline (2020-2023)

Traditional RAG follows a simple linear flow:

• User Query → Embedding → Vector Search → Context Injection → LLM → Response

Step-by-Step Process

• Document Ingestion (Offline)

# Chunk documents into passages

chunks = split_document(doc, chunk_size=512, overlap=50)

# Generate embeddings

for chunk in chunks:

embedding = embedding_model.encode(chunk.text)

vector_db.insert(chunk.id, embedding, metadata=chunk.metadata)

• Query Processing (Online)

# User asks: "What's the company vacation policy?"

query_embedding = embedding_model.encode(user_query)

# Retrieve top-k similar chunks

results = vector_db.similarity_search(query_embedding, k=5)

# Inject context into LLM prompt

context = "\n".join([r.text for r in results])

prompt = f"Given context:\n{context}\n\nAnswer: {user_query}"

# Generate answer

response = llm.generate(prompt)

• Response:
• • "According to the employee handbook, employees receive 15 days PTO annually..." [Citations: Employee Handbook p.23]

Strengths of Traditional RAG

• Simple: Easy to implement with libraries like LangChain, LlamaIndex
• Fast: Single LLM call (~500ms latency)
• Affordable: Minimal compute overhead
• Proven: Works well for simple Q&A on small-to-medium datasets

Critical Weaknesses

• No Verification: If the vector search retrieves irrelevant documents, the LLM hallucinates. There's no quality control.

Example:

• Query: "Who approved the Q3 budget?"
• Retrieved (incorrectly): Q2 budget approval email
• LLM Response: "Jane Doe approved the Q3 budget on April 15." ← Wrong! (That was Q2)
• One-Shot Execution: No self-correction. If retrieval fails, the answer is wrong.
• No Relationship Understanding: Cannot answer "Which contracts depend on this budget?" Requires reading individual documents.
• Limited to Text: Cannot generate structured outputs (tables, charts).
• No Compliance Enforcement: Cannot enforce RBAC or redact PII.

When Traditional RAG Is Sufficient

• Small datasets (<10,000 documents)
• Simple queries (single-hop, factual Q&A)
• Non-critical use cases (internal FAQs, basic support)
• Tolerant of errors (accuracy <90% acceptable)

How Agentic RAG Works

(Docmet Implementation)

The Multi-Agent RAG Architecture (2024+)

Agentic RAG replaces the linear pipeline with a graph-based orchestration system where specialized agents collaborate.

• User Query → Planner Agent → [Retriever → Grader → Generator] (loop) → Response → Tool Executor, Compliance, Conflict Detector (parallel)

Agent Roles (LangGraph Implementation)

1. Planner Agent

Responsibility: Decompose complex queries into sub-tasks

Example:

• Query: "Which engineering projects depend on budgets that were cut in Q3?"
• Plan:
• • Retrieve Q3 budget document
  • Identify cut line items
  • Search for projects mentioning those budgets
  • Cross-reference project owners

class PlannerAgent:

def plan(self, query: str) -> List[Task]:

prompt = f"""

Decompose this query into executable sub-tasks:

Query: {query}

Output JSON: [{{"task": "...", "depends_on": []}}]

"""

plan = llm.generate(prompt, output_format="json")

return self.validate_plan(plan)

2. Retriever Agent

Responsibility: Hybrid search (keyword + semantic + graph traversal)

Hybrid Search Strategy:

class RetrieverAgent:

def retrieve(self, task: Task) -> List[Document]:

# 1. Keyword search (fast, high precision)

keyword_results = elastic_search(task.keywords)

# 2. Semantic search (captures synonyms, paraphrases)

embedding = embed(task.natural_language)

semantic_results = vector_db.search(embedding, k=10)

# 3. Graph traversal (follows relationships)

if task.requires_graph:

entities = extract_entities(task.query)

graph_results = neo4j.traverse(

start=entities,

relationship_types=["DEPENDS_ON", "APPROVED_BY"],

depth=3

)

# 4. Fusion ranking (combine results)

return self.rerank(keyword_results + semantic_results + graph_results)

GraphRAG Addition:

• Traditional RAG stops at document retrieval
• GraphRAG follows entity relationships: "Project Alpha" → DEPENDS_ON → "Q3 Budget" → APPROVED_BY → "Jane Doe"

3. Grader Agent (CRAG - Corrective RAG)

Responsibility: Score document relevance; trigger re-retrieval if quality is low

CRAG Loop:

class GraderAgent:

def grade(self, documents: List[Document], query: str) -> GradeResult:

scores = []

for doc in documents:

prompt = f"""

Score this document's relevance to the query (0-10):

Query: {query}

Document: {doc.text[:500]}...

Criteria:

- 9-10: Directly answers query

- 7-8: Contains relevant info

- 4-6: Tangentially related

- 0-3: Irrelevant

"""

score = llm.generate(prompt, output_format="int")

scores.append(score)

avg_score = np.mean(scores)

if avg_score < 7.0:

return GradeResult(

status="RETRY",

reason="Low relevance, refining query...",

refined_query=self.refine_query(query, documents)

)

return GradeResult(status="PASS", documents=documents)

Self-Correction:

• If Grader rejects documents, Planner refines the query
• Max 3 CRAG iterations (prevents infinite loops)
• Fallback: Ask user for clarification

4. Generator Agent

Responsibility: Create final answer + A2UI components

A2UI (Agent-to-UI) Generation:

class GeneratorAgent:

def generate(self, context: str, query: str) -> Response:

# Decide output format

format = self.detect_format(query)

# "compare X vs Y" → table

# "trend over time" → chart

# "approve this" → workflow

if format == "table":

return self.generate_table(context, query)

elif format == "chart":

return self.generate_chart(context, query)

else:

return self.generate_text(context, query)

def generate_table(self, context: str, query: str):

prompt = f"""

Extract structured data from context and create table.

Context: {context}

Query: {query}

Output JSON:

{{

"columns": ["Project", "Budget", "Status"],

"rows": [[...], [...]]

}}

"""

table_json = llm.generate(prompt, output_format="json")

return Response(type="table", data=table_json)

Why This Matters:

• Traditional RAG: "Project A had budget $500K, Project B had $300K..."
• Agentic RAG: Generates sortable, interactive table with drill-down

5. Compliance Agent

Responsibility: Enforce RBAC, redact PII

class ComplianceAgent:

def enforce(self, documents: List[Document], user: User) -> List[Document]:

filtered = []

for doc in documents:

# Check RBAC

if not self.has_permission(user, doc):

continue # Redact entire document

# Redact PII at paragraph level

doc.text = self.redact_pii(doc.text, user.clearance_level)

filtered.append(doc)

# Audit log

self.log_access(user.id, [d.id for d in filtered])

return filtered

6. Conflict Detector Agent

Responsibility: Identify contradictions between sources

Example:

• Doc A (2024-01-15): "Q3 Budget: $1.0M"
• Doc B (2024-03-20): "Q3 Budget: $1.2M"

Output:

{

"conflict_detected": true,

"sources": [

{"doc": "Budget v1", "date": "2024-01-15", "value": "$1.0M"},

{"doc": "Budget v2", "date": "2024-03-20", "value": "$1.2M"}

],

"recommendation": "Use most recent (Budget v2) but flag discrepancy"

}

7. Tool Executor Agent

Responsibility: Execute SQL, API calls, calculations

Example:

• Query: "What's 10% of Q3 revenue?"
• Tool Executor: Executes SELECT revenue FROM financials WHERE quarter='Q3' → Calculates 10%

class ToolExecutorAgent:

def execute(self, tool_call: ToolCall) -> Any:

if tool_call.type == "sql":

return self.execute_sql(tool_call.query)

elif tool_call.type == "api":

return self.call_api(tool_call.endpoint, tool_call.params)

elif tool_call.type == "calculation":

return eval(tool_call.expression) # Sandboxed!

Orchestration with LangGraph

from langgraph.graph import StateGraph

workflow = StateGraph()

# Define nodes (agents)

workflow.add_node("planner", PlannerAgent())

workflow.add_node("retriever", RetrieverAgent())

workflow.add_node("grader", GraderAgent())

workflow.add_node("generator", GeneratorAgent())

workflow.add_node("compliance", ComplianceAgent())

# Define edges (control flow)

workflow.add_edge("planner", "retriever")

workflow.add_edge("retriever", "grader")

# Conditional edge: CRAG loop

workflow.add_conditional_edge(

"grader",

lambda state: "retriever" if state["grade"] == "RETRY" else "generator",

max_loops=3

)

workflow.add_edge("generator", "compliance")

workflow.set_entry_point("planner")

# Compile

app = workflow.compile()

# Execute

result = app.invoke({"query": user_query})

Decision Framework

Choosing the Right RAG Architecture for Your Use Case

Choose Traditional RAG If:

• Budget-Constrained Prototype
• • Early-stage experiments where accuracy <90% is acceptable
  • Internal tools with low stakes (e.g., employee FAQ bot)
• Small Dataset (<10,000 documents)
• • Traditional RAG works fine at this scale
  • Complexity of agents isn't justified
• Simple Q&A Only
• • Single-hop, factual queries: "What's our refund policy?"
  • No multi-hop reasoning required
• Latency Is Critical
• • Need <500ms response time
  • Cannot tolerate 2-second latency of CRAG loops
• Non-Regulated Industry
• • No compliance requirements
  • Hallucinations are annoying but not catastrophic

Choose Agentic RAG If:

• Regulated Industry (Healthcare, Finance, Legal)
• • Accuracy <99% is unacceptable
  • Hallucinations could trigger regulatory violations ($100K+ fines)
• Complex Queries (Multi-Hop Reasoning)
• • Questions like "Find contracts depending on cut budgets approved by X"
  • Require relationship traversal across documents
• Large Dataset (50,000+ documents)
• • Traditional RAG's retrieval quality degrades at scale
  • GraphRAG's relationship understanding becomes essential
• Structured Outputs Required
• • Need tables, charts, workflows (not just text)
  • A2UI generation is critical
• Compliance & Audit Trails
• • Must prove AI decisions are traceable
  • RBAC enforcement and PII redaction required
• Proactive Intelligence
• • Want AI to flag stale content, detect conflicts, alert on issues
  • Traditional RAG is reactive only

Decision Matrix

• Accuracy Requirement:
• • Traditional RAG: <90%
  • Agentic RAG: >99%
• Dataset Size:
• • Traditional RAG: <10K docs
  • Agentic RAG: >50K docs
• Query Complexity:
• • Traditional RAG: Single-hop
  • Agentic RAG: Multi-hop
• Budget:
• • Traditional RAG: Low
  • Agentic RAG: Moderate
• Compliance:
• • Traditional RAG: Non-regulated
  • Agentic RAG: Regulated
• Latency Tolerance:
• • Traditional RAG: <500ms
  • Agentic RAG: <3s
• Output Format:
• • Traditional RAG: Text only
  • Agentic RAG: Tables/charts

Building Agentic RAG Systems

Technical Requirements for Agentic RAG

Infrastructure

1. Vector Database

• Options: Pinecone, Weaviate, Qdrant, Milvus
• Requirements: Support for hybrid search (keyword + semantic)
• Scale: Must handle 100K+ vectors with <100ms latency

2. Graph Database

• Options: Neo4j, TigerGraph, ArangoDB
• Requirements: Efficient multi-hop traversal (BFS/DFS)
• Scale: Handle 1M+ entities and 10M+ relationships

3. Orchestration Framework

• Options: LangGraph, CrewAI, AutoGen, Semantic Kernel
• Requirements: State management, conditional edges, observability
• Docmet uses: Custom LangGraph implementation

4. Observability

• Requirements: Trace every agent decision (input state, output state, latency)
• Tools: LangSmith, Weights & Biases, custom Grafana dashboards

Development Complexity

Traditional RAG:

• Engineer Time: 2-4 weeks (experienced team)
• Skill Level: Mid-level ML engineer
• Libraries: LangChain, LlamaIndex (high-level abstractions)

Agentic RAG:

• Engineer Time: 3-6 months (experienced team)
• Skill Level: Senior ML engineer + software architect
• Libraries: LangGraph (lower-level, requires orchestration design)
• Additional Complexity: State management, CRAG loop logic, agent coordination

Cost Considerations

Inference Costs:

• Traditional RAG: ~$0.02/query (1 LLM call)
• Agentic RAG: ~$0.09/query (7 agents, 1.3 CRAG loops)
• Scaling: At 100K queries/month, difference is $2,000/mo vs $9,000/mo

Development Costs:

• Traditional RAG: $50K-$100K (2-4 weeks × 2 engineers)
• Agentic RAG: $300K-$500K (3-6 months × 3 engineers + architect)

Total Cost of Ownership (3 years):

• Traditional RAG: ~$172K (dev + inference + maintenance)
• Agentic RAG: ~$824K (dev + inference + maintenance)

ROI Justification:

• If a single compliance violation costs $500K+ (common in healthcare/finance), Agentic RAG's 99% accuracy pays for itself
• 70% time savings × 100 employees × $50/hr = $1.4M/year value

Agentic RAG in Production

Case Study: Global Law Firm

Challenge: 500 attorneys needed to search 2M legal documents (contracts, case law, briefs) to answer client questions with verifiable accuracy.

Traditional RAG Results (Pilot):

• Accuracy: 83% (17% contained citation errors or hallucinations)
• Rejection Rate: 40% of attorneys didn't trust AI, reverted to manual search
• Risk: One incorrect citation in a court filing could cost the case

Agentic RAG Results (Docmet Implementation):

• Accuracy: 99.4% (CRAG eliminated most errors)
• Adoption: 92% of attorneys use daily
• Time Savings: 18 hours/week per attorney (previously spent on manual research)
• ROI: $12M/year value (time savings × hourly rate)

Technical Implementation

GraphRAG Knowledge Graph:

• Entities: Cases, statutes, contracts, parties, judges, precedents
• Relationships: CITES, OVERRULES, APPLIES_TO, CONFLICTS_WITH
• Example Query: "Find contracts citing cases that were overruled after 2020"
• • Traditional RAG: Cannot handle this
  • Agentic RAG: Traverses graph path: Contracts → CITES → Cases → OVERRULED → Date filter

CRAG Verification:

• Grader Agent scores retrieved cases for relevance to query
• If score <8/10, refines search query (e.g., adds jurisdiction constraint)
• Result: 99%+ of citations verified as accurate before presenting to attorney

Compliance Agent:

• Enforces attorney-client privilege
• Redacts confidential client information before sharing across matters
• Logs all document access for audit (bar association requirements)

Results After 12 Months

• Accuracy:
• • Before (Traditional RAG): 83%
  • After (Agentic RAG): 99.4%
• Attorney Trust:
• • Before (Traditional RAG): 60%
  • After (Agentic RAG): 92%
• Time per Research Task:
• • Before (Traditional RAG): 4.5 hours
  • After (Agentic RAG): 0.8 hours
• Monthly Queries:
• • Before (Traditional RAG): 12,000
  • After (Agentic RAG): 48,000 (4× increase due to trust)
• Compliance Violations:
• • Before (Traditional RAG): 2 (PII leakage)
  • After (Agentic RAG): 0
• ROI:
• • Before (Traditional RAG): Break-even
  • After (Agentic RAG): $12M/year positive